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Spm unit v-software reliability-

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Spm unit v-software reliability-

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Spm unit v-software reliability-

  1. 1. SPM-UNIT - V SOFTWARE RELIABILITY Prof. Kanchana Devi
  2. 2. Software Reliability Prof. Kanchana Devi 2  Categorising and specifying the reliability of software systems  Informal Definition:  Reliability is a measure of how well system users think it provides the services they require.  Probability of a software component will produce an incorrect output  Software can continue to operate after a bad result
  3. 3. ……… Prof. Kanchana Devi 3  Software Reliability is the probability of failure- free software operation for a specified period of time in a specified environment.  Software Reliability is also an important factor affecting system reliability.  It differs from hardware reliability in that it reflects the “design perfection”, rather than manufacturing perfection.  The high complexity of software is the major contributing factor of Software Reliability problems.
  4. 4. Traditional Methods For Improving Software Reliability Prof. Kanchana Devi 4  Three main techniques are used in industrial and open source projects to improve software reliability:  Manual Testing  Code Reviews:  Modifications are reviewed by experienced developers before being committed to the code base.  Coding Standards:  Requiring that all developers adhere to a set of rules when writing or maintaining code.  Coding standards can improve source code readability, making it easier to spot defects, and  Ban the use of programming idioms that are arguably dangerous.
  5. 5. Reliability Problems Prof. Kanchana Devi 5  They depend fundamentally on human reasoning and judgments  They do not provide guarantees
  6. 6. Measuring Reliability Prof. Kanchana Devi 6  A simple measure of reliability can be given as:  MTBF = MTTF + MTTR , where  MTBF is mean time between failures  MTTF is mean time to fail  MTTR is mean time to repair
  7. 7. Software Reliability Models Prof. Kanchana Devi 7  Error Seeding  Reliability growth  Non-Homogeneous Poisson process (NHPP)
  8. 8. Error Seeding Prof. Kanchana Devi 8  Estimates the number of errors in a program.  Errors are divided into  Indigenous Errors  Induced (seeded) Errors.  The unknown number of indigenous errors is estimated from the number of induced errors.  The ratio of the two types of errors obtained from the testing data.
  9. 9. Reliability Growth 9  Measures and predicts the improvement of reliability through the testing process using a growth function to represent the process.  Growth Function has two types of variables:  Independent Variables  Dependent Variables  Independent Variables of the growth function could be time and number of test cases (or testing stages)  Dependent Variables can be reliability, failure rate or cumulative number of errors detected.
  10. 10. Non-homogeneous Poisson process (NHPP) Prof. Kanchana Devi 10  Provide an analytical framework for describing the software failure phenomenon during testing.  The main issue is to estimate the mean value function of the cumulative number of failures.
  11. 11. Prof. Kanchana Devi 11 ) ],[infailuresofNumber ()(       f  A typical measure (failures per unit time) is the failure intensity (rate) given as:  where  = program CPU time (in a time shared computer) or wall clock time (in an embedded system).
  12. 12. Issues in SR: Prof. Kanchana Devi 12  SR Growth models are generally “black box” - no easy way to account for a change in the “operational profile”  “Operational profile”: description of the input events expected to occur in actual software operation – how it will be used in practice
  13. 13. Prof. Kanchana Devi 13  Many models have been proposed, perhaps the most prominent are:  Musa Basic model  Musa/Okomoto Logarithmic model  Some models work better than others depending on the application area and operating characteristics:  i.e. interactive?  data intensive?  control intensive?  real-time?
  14. 14. Choice of Model - Basic Model: Prof. Kanchana Devi 14  For studies or predictions before execution and failure data available  Using study of faults to determine effects of a new software engineering technology  The program size is changing continually or substantially (i.e. during integration)
  15. 15. Logarithmic Model Prof. Kanchana Devi 15  System subjected to highly non-uniform operational profiles.  Highly predictive validity is needed early in the execution period.  The rapidly changing slope of the failure intensity during early stages can be better fitted with the Logarithmic Poisson than the basic model .

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